The present invention relates to a method and apparatus for determining the cerebral state of a patient. One application of the method and apparatus is determining the extent of a hypnotic state of the patient resulting, for example, from the administration of an anesthetic agent. That extent is often termed the “depth of anesthesia.” In the method and apparatus of the present invention, changes in the cerebral state can be accurately and quickly determined.
In a simplistic definition, anesthesia is an artificially induced state of partial or total loss of sensation or pain, i.e. analgesia. For most medical procedures the loss of sensation is accompanied by a loss of consciousness on the part of a patient so that the patient is amnestic and is not aware of the procedure.
The “depth of anesthesia” generally describes the extent to which consciousness is lost following administration of an anesthetic agent. As the magnitude of anesthetization, or depth of anesthesia, increases, an anesthetized patient typically fails to successively respond to spoken commands, loses the eyelid reflex, loses other reflexes, undergoes depression of vital signs, and the like.
While loss of consciousness (hypnosis, amnesia) and the loss of sensation (analgesia) are significant features of anesthesia, it should be noted that balanced high quality anesthesia must also consider muscle relaxation, suppression of the autonomous nervous system, and blockade of the neuro muscular junction. Sufficient muscle relaxation is required to ensure optimal operating conditions for the surgeon manipulating the patient's tissue. The autonomous nervous system, if not suppressed, causes the patient to respond to surgical activity with a shock reaction that effects heavily on hemodynamics and the endocrine system. To keep the patient completely motionless, the neuro muscular junctions transmitting orders from the brain to the muscles of the body need to be blocked so that the body of the patient becomes completely paralyzed.
While the need to determine the state of all five components of anesthesia is widely recognized, ascertaining and quantifying the state of hypnosis or depth of anesthesia in a reliable, accurate, and quick manner has been, and is, the subject of extensive attention. One reason for this is its importance. If the anesthesia is not sufficiently deep, the patient may maintain or gain consciousness during a surgery, or other medical procedure, resulting in an extremely traumatic experience for the patient, anesthesiologist, and surgeon. On the other hand, excessively deep anesthesia reflects an unnecessary consumption of anesthetic agents, most of which are expensive. Anesthesia that is too deep requires increased medical supervision during the surgery recovery process and prolongs the period required for the patient to become completely free of the effects of the anesthetic agent. A second reason for the continuing study and attention being given to monitoring the hypnotic condition of a patient arises because of its difficulty: that is, anesthetic agents alter the activity and state of the patient's brain and these changes are not always easy to detect.
A measure of the depth of anesthesia that may be used for research purposes is found in an Observer's Assessment of Alertness and Sedation or OAAS. The OAAS determines the level of consciousness or, conversely, the depth of sedation or anesthesia, based on a patient's response to external stimuli. One such assessment that classifies the depth of anesthesia in six levels, is summarized by the table below. The transition from consciousness to unconsciousness may be deemed to occur when the OAAS score changes from level 3 to level 2. Level zero corresponds to a state of deep anesthesia in which the patient shows no response to a very painful stimulus.
OAASScoreDistinctive Characteristics5Patient replies readily to spoken commands, eyes open, awake.4Patient is sedated, but replies to spoken commands, mild ptosis.3Patient ceases to reply to loud commands, eye lid reflect present.2Patient does not reply to spoken commands, no eye lid reflex.1Patient does not react to TOF stimulation (50 mA) withmovement.0Patient does not react to tetanic stimulation with movement.
“Ptosis” is a drooping of the upper eyelids. “TOF stimulation” (“train-of-four”) is a very short, painful electrical (50 mA) stimulus applied to the ulnar nerve in the arm of the patient, repeated four times to evaluate the intensity of muscular contraction. In “tetanic stimulation” the electrical current (50 mA) is applied continuously for a period of time, such as 5 seconds. The ulnar nerve is the nerve which, when pinched, gives rise to the well known “crazy or funny bone” effect.
While useful for research and other purposes, an OAAS scale provides only a limited number of scaling levels and is limited in practical use because of the attention required from the anesthesiologist and the use of painful stimuli.
It has long been known that the neurological activity of the brain is reflected in biopotentials available on the surface of the brain and on the scalp. Thus, efforts to quantify the extent of anesthesia induced hypnosis have turned to a study of these biopotentials. The biopotential electrical signals are usually obtained by a pair, or plurality of pairs, of electrodes placed on the patient's scalp at locations designated by a recognized protocol and a set, or a plurality of sets or channels, of electrical signals are obtained from the electrodes. These signals are amplified and filtered. The recorded signals comprise an electroencephalogram or EEG.
Among the purposes of filtering is to remove electromyographic (EMG) signals from the EEG signal. EMG signals result from muscle activity of the patient and will appear in electroencephalographic electrodes applied to the forehead or scalp of the patient. They are usually considered artifacts with respect to the EEG signals. Since EMG signals characteristically have most of their energy in a frequency range (40–300 Hz) which is different than that of the EEG, major portions of the EMG signals can be separated from the EEG signal.
A typical EEG is shown in FIG. 1. A macro characteristic of EEG signal patterns is the existence of broadly defined low frequency rhythms or waves occurring in certain frequency bands. Four such bands are recognized: Delta (0.5–3.5 Hz), Theta (3.5–7.0 Hz), Alpha (7.0–13.0 Hz) and Beta (13.0–32.0 Hz). Alpha waves are found during periods of wakefulness and may disappear entirely during sleep. The higher frequency Beta waves are recorded during periods of intense activation of the central nervous system. The lower frequency Theta and Delta waves reflect drowsiness and periods of deep sleep.
By analogy to the depth of sleep, it can be said that the frequency of the EEG will decrease as the depth of anesthesia increases, while the magnitude of the signal initially often increases. However, this gross characterization is too imprecise and unreliable to use as an indication of such a critical medical aspect as the extent of hypnosis. Further, EEG signal changes during anesthesia may not fully correlate with changes in the hypnotic state of the patient. For example, it has been reported that in a 12–18 Hz frequency band, EEG activity initially increases as anesthetic agents are administered and only thereafter decreases as anesthesia deepens.
The foregoing circumstance has led to the investigation and use of other techniques to study EEG waveforms to ascertain the underlying condition of the brain, including the depth of anesthesia to which a patient is subjected. It will be immediately appreciated from FIG. 1 that EEG signals are highly random in nature. Unlike other biopotential signals, such as those of an electrocardiogram (ECG), an EEG normally has no obvious repetitive patterns, the morphology and timing of which can be conveniently compared and analyzed. Nor does the shape of the EEG waveform correlate well to specific underlying events in the brain. Hence, except for certain phenomena, such as epileptic seizures, which are readily apparent from visual inspection of an EEG, the indication of other conditions in the brain in the EEG is much more subtle.
Prefatory to the use of other techniques, the EEG signals are subjected to analog to digital signal conversion by sequentially sampling the magnitude of the analog EEG signals and converting same to a series of digital data values. The sampling is typically carried out at a rate of 100 Hz or greater. The digital signals are stored in the magnetic or other storage medium of a computer and then subjected to further processing to ascertain the underlying state of the brain. Such processing typically uses sets of sequential EEG signal samples or data points representing a finite block of time, commonly termed an “epoch.” The analysis of the data is usually carried out on a moving average basis employing a given epoch and a certain number of backward epochs.
Some of the techniques by which EEG signals can be analyzed in an effort to determine the depth of anesthesia are well described in Ira J. Rampil, A Primer for EEG Signal Processing in Anesthesia, Vol. 89, Anesthesiology No. 4, pgs. 980 et seq., October 1998.
One such technique is to examine, in some meaningful way, how the voltage of an EEG signal changes over time. Such an analysis is termed a “time-domain analysis.” Because of its generally random nature, an EEG signal is not a deterministic signal. This means that it is not possible to exactly predict future values of the EEG from past values in the manner that, for example, the shapes of past QRS complexes in an ECG signal can be used to predict future values for analytical and diagnostic purposes. However, certain statistical characteristics of random signals, such as an EEG, can be determined and used for analytic purposes.
Time-domain based EEG analysis methods have not proven greatly successful in clinical applications since the results do not behave in a completely consistent manner. However, such methods have been reported in the use of an electrical power parameter derived from the time-domain EEG signal voltage to control administration of an anesthetic agent. Combinations of time-domain based statistic parameters have been used to analyze EEG data. Efforts have also been made to use the number of times the EEG signal crosses the zero voltage level in a given period to analyze EEG signal data.
Time-domain based analysis is however useful in the study and quantification of burst suppression in the EEG. During deep sleep or anesthesia, the EEG time-domain signal may develop a pattern of activity which is characterized by alternating periods or “bursts” of normal, or high frequency and amplitude, voltage signals and periods of low or no voltage, which periods are termed those of “suppression.” The extent of this phenomenon can be expressed as a “burst suppression ratio (BSR)” which is a time domain EEG parameter describing the time the EEG voltage is in the suppressed state as a fraction of the sampling period.
A second approach to analyzing EEG waveforms examines signal activity as a function of frequency, i.e. a “frequency-domain analysis.” It has long been recognized that complex waveforms, such as EEG signals, can be decomposed, or transformed, into a plurality, or spectrum, of simple sine or cosine waves of various frequencies, amplitudes, and phases. Frequency-domain spectra can be obtained from sequential time-domain EEG signal data by a Fourier transform. Frequency-domain analysis analyzes the spectrum of frequency signals obtained from the transform to determine characteristics and features occurring in wave forms having the various frequencies of the spectrum. The results of an EEG frequency-domain analysis are typically graphically displayed as a power versus frequency histogram in which frequency is graphed on the abscissa and power is graphed on the ordinate.
Further efforts to obtain useful information from electroencephalograms have employed higher order analyses, including the bispectrum and trispectrum. The bispectrum, which measures the correlation of phase between two different frequency components and quantifies the relationships among the underlying sinusoidal components of the EEG, has received considerable attention. The bispectrum specifically quantifies the relationship between sinusoids at two primary frequencies f1 and f2 and a modulation component at the frequency f1+f2. A strong phase relationship between f1, f2 and f1+f2 creates a large bispectral value for frequency f1+f2. However, because the calculation must be performed using complex number arithmetic for several thousand f1, f2, and f1+f2 frequency combinations, the computations to obtain bispectral information are rather arduous.
For clinical use, it is desirable to simplify the results of EEG signal analysis of the foregoing, and other types, into a workable parameter that can be used by an anesthesiologist in a clinical setting when attending the patient. Ideally, what is desired is a simple, single parameter or index that quantifies the depth of anesthesia on a consistent, continuous scale extending from full alertness to maximally deep, but reversible, hypnosis. To be fully useful such a scale should maintain its consistency, notwithstanding the differing pharmacological effects of different anesthetic agents, as well as the differing physiologies of different patients.
Various such parameters for relating EEG signal data to the hypnotic state of the patient are discussed in the literature. Several use frequency domain power spectral analysis, These parameters include peak power frequency (PPF), median power frequency (MPF), and spectral edge frequency (SEF). A peak power frequency (PPF) parameter uses the frequency in a spectrum at which occurs the highest power in the sampled data as an indication of the depth of anesthesia. The median power frequency (MPF) parameter, as its name implies, uses the frequency that bisects the spectrum. In the same fashion, the spectral edge frequency uses the highest frequency in the EEG signal. A modification of the latter is the SEF 95 parameter, which is the frequency below which 95% of the power in the spectrum resides.
To improve the consistency of an indicator of the hypnotic state or depth of anesthesia, several parameters are often employed in combination. For example, the spectral edge frequency (SEF) parameter may be combined with the time-domain burst suppression ratio (BSR) parameter to improve the consistency and accuracy with which the depth of anesthesia can be indicated.
While parameters of the foregoing types can detect changes in the EEG caused by anesthetic agents and hence are useful in determining the depth of anesthesia, they suffer from an inability to be calibrated to behavioral endpoints and because of their sensitivity to the different EEG patterns induced by different anesthetic agents.
More complex combinations of parameters are described in U.S. Pat. Nos. 4,907,597; 5,010,891; 5,320,109; and 5,458,117 to Nassib Chamoun or Chamoun et al. and are employed in the anesthesia monitor product made and sold by the assignee of the patents, Aspect Medical Systems of Framingham, Mass. The patents describe various combinations of a time-domain subparameter and frequency-domain subparameters, including a high order spectral subparameter, to form a single variable, termed the bispectral index (BIS), that correlates behavioral assessments of sedation and hypnosis over a range of anesthesia for several anesthetic agents. Because of this ability, the Aspect Medical Systems product has found clinical acceptance.
The bispectral index, BIS, consists of the following three subcomponents: SyncFastSlow, BetaRatio, and Burst Suppression. The calculation of the subparameter SyncFastSlow utilizes bispectral analysis in the frequency-domain. The SyncFastSlow parameter corresponds to the logarithm of the ratio of the sum of all bispectral peaks in the frequency range 0.5–47 Hz divided by the sum in the range 40–47 Hz. The bispectral information in the SyncFastSlow subparameter does not, by itself, give sufficient information over the range of hypnosis thus requiring combination with the other subparameters. The BetaRatio subparameter gives the logarithm of the power ratio in the frequency ranges 30–47 Hz and 11–20 Hz. It is a frequency-domain parameter that has been found to work best in light sedation. As noted above, in very deep levels of anesthesia, EEG signal contains data samples in which the EEG activity is suppressed. The Burst Suppression Ratio obtained from a time-domain analysis of the EEG signal describes the relative content of burst and suppression in the signal. The Burst Suppression Ratio is operative in deep anesthesia in which the suppression occurs.
The resulting bispectral index, BIS, is a combination of these three subparameters. The combining algorithm weights the different subparameters according to their range of best performance. While the details of the algorithm are unpublished and proprietary, it is known that different subparameters or combination of subparameters are employed depending on the level of hypnosis or depth of anesthesia. For example, light sedation, it is necessary to use the bispectral SyncFastSlow subparameter in conjunction with the BetaRatio subparameter in order produce reliable results. For deep anesthesia it is necessary to combine the bispectral subparameter SyncFastSlow with the Burst Suppression Ratio subparameter to produce reliable results. The algorithm appears circuitous in that in order to make the proper combination of subparameters required to accurately determine the depth of anesthesia, the algorithm must know what the level of anesthesia is which, in turn, requires the proper subparameter combination.
Certain paradoxical behavior of the bispectral index (BIS) has been reported. See Detsch, et al. “Increasing Isoflurane Concentration may cause Paradoxical Increases in the EEG bispectral index in Surgical Patients”, Br. J. Anaesth. 84 (2000), pgs. 33–37. Because the index uses a plurality of subparameters and combinations thereof in different regions of hypnosis, this behavior may occur when the hypnotic level of a patient is at a boundary of the regions, for example, in the range between “surgical levels” and “deep hypnosis.”
Further, computation of the bispectral index (BIS) parameter requires averaging several epochs of EEG data. Thus, this index may be not sufficiently fast to detect changes in the state of a patient as is required in the clinical situation. See, Baker, et al. Electroencephalographic Indices Related to Hypnosis and Amnesia During Propofol Anaesthesia for Cardioversion, Anaesthesia and Intensive Care, Vol. 28, No. 4, 2000. Hence, the BIS index may indicate recovery several seconds after a patient has already opened his/her eyes. This can be a serious problem in the use of the BIS index. By knowing the depth of anesthesia, the anesthesiologist can more precisely control the amount of anesthetic agent administered to a patient. Often this results in a reduction in the amount of agent administered. However, the lessened amount of anesthetic agent increases the risk that the patient will awaken during surgery. It is therefore essential that an anesthesiologist knows immediately if a patient is approaching consciousness out of the hypnotic state.
A different approach to the analysis of electroencephalographic signals is to attempt to quantify the complexity of the highly random EEG signal for use as an indication of the depth of anesthesia. This approach is based on the premise that neuronal systems, such as those of the brain, have been shown to exhibit a variety of non-linear behaviors so that measures based on the non-linear dynamics of the EEG signal should allow direct insight into the state of the underlying brain activity.
There are a number of concepts and analytical techniques directed to the complex nature of random and unpredictable signals. One such concept is entropy. Entropy, as a physical concept, describes the state of disorder of a physical system. When used in signal analysis, entropy addresses and describes the complexity, unpredictability, or randomness characteristics of a signal. In a simple example, a signal in which sequential values are alternately of one fixed magnitude and then of another fixed magnitude has an entropy of zero, i.e. the signal is totally predictable. A signal in which sequential values are generated by a random number generator has greater complexity and a higher entropy.
Applying the concept of entropy to the brain, the premise is that when a person is awake, the mind is full of activity and hence the state of the brain is more non-linear, complex, and noise like. Since EEG signals reflect the underlying state of brain activity, this is reflected in relatively more “randomness” or “complexity” in the EEG signal data, or, conversely, in a low level of “order.” As a person falls asleep or is anesthetized, the brain function begins to lessen and becomes more orderly and regular. As the activity state of the brain changes, this is reflected in the EEG signals by a relative lowering of the “randomness” or “complexity” of the EEG signal data, or conversely, increasing “order” in the signal data. When a person is awake, the EEG data signals will have higher entropy and when the person is asleep the EEG signal data will have a lower entropy.
With respect to anesthesia, an increasing body of evidence shows that EEG signal data contains more “order”, i.e. less “randomness”, and lower entropy, at higher concentrations of an anesthetic agent, i.e. greater depth of anesthesia, than at lower concentrations. At a lower concentration of anesthetic agent, the EEG signal has higher entropy. This is due, presumably, to lesser levels of brain activity in the former state than in the latter state. See “Stochastic complexity measures for physiological signal analysis” by I. A. Rezek and S. J. Roberts in IEEE Transactions on Biomedical Engineering, Vol. 4, No. 9, September 1998 describing entropy measurement to a cut off frequency of 25 Hz and Bruhn, et al. “Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesia”, Anesthesiology, 92 (2000), pgs. 715–726 describing entropy measurement in a frequency range of 0.5 to 32 Hz. See also H. Viertiö-Oja et al. “New method to determine depth of anesthesia from EEG measurement” in J. Clin. Monitoring and Comp. Vol. 16 (2000) pg. 16 which reports that the transition from consciousness to unconsciousness takes place at a universal critical value of entropy which is independent of the patient.
The pertinence of the concept of entropy to the conscious and unconscious states of the brain is also supported in recent theoretical work (see Steyn-Ross et al., Phys. Rev. E60 1999, pgs. 7229–7311) which applies thermodynamic theory to the study of the brain. This work points to the conclusion that when a patient undergoing anesthetization passes from the conscious state to the unconscious state, a thermodynamic phase transition of the neural system of the brain takes place which is roughly analogous to the phase change occurring when water freezes into ice. During the process of freezing, an amount of entropy, proportional to the latent heat of the process is removed so that water and ice have different entropies. The conscious and unconscious states of the brain may therefore similarly be expected to have distinct, different values of entropy. The premise that loss of consciousness can be regarded as analogous to a thermodynamic phase transition, lends further support to the concept of entropy as a fundamental characteristic of the cerebral state of the brain and to the use of entropy in determining depth of anesthesia as employing a quantity reflecting the basic mechanisms of the brain rather than derived phenomena, such as power spectra, reflecting those mechanisms.
In sum, the following can be said. First, certain forms of entropy have generally been found to behave consistently as a function of anesthetic depth. See Bruhn et al. and H. E. Viertiö-Oja et al. “Entropy of EEG signal is a robust index for depth of hypnosis”, Anesthesiology 93 (2000) A, pg. 1369. This warrants consideration of entropy as a natural and robust choice to characterize levels of hypnosis. Also, because entropy correlates with depth of anesthesia at all levels of anesthesia, it avoids the need to combine various subparameters as in the bispectral index (BIS). Second, the transition from consciousness to unconsciousness takes place at a critical level of entropy which is independent of the patient. See Viertio-Oja et al. in J. Clin. Monitoring and Computing. Thirdly, and of particular practical significance, recovery of a patient toward consciousness from anesthesia can often be predicted by a rise of entropy toward the critical level.
A number of techniques and associated algorithms are available for quantifying signal complexity, including those based on entropy, as described in the Rezek and Roberts article in IEEE Transactions on Biomedical Engineering article. One such algorithm is that which produces spectral entropy for which the entropy values are computed in frequency space. Another algorithm provides approximate entropy which is derived from the Kolmogorov-Sinai entropy formula and computed in Taken's embedding space. See Steven M. Pincus, Igor M. Gladstone, and Richard A. Ehrenkranz, “A regularity statistic for medical data analysis”, J. Clin. Monitoring 7 (1991), pgs. 335–345. A program for computing approximate entropy is set out in the Bruhn et al., article in Anesthesiology. The spectral entropy and approximate entropy techniques have found use in analyzing the complexity of EEG signal data.
Another technique for non-linear analysis of highly random signals is expressed in Lempel-Ziv complexity in which the complexity of a string of data points is given by the number of bytes needed to make the shortest possible computer program which is able to generate the string. See Abraham Lempel and Jacob Ziv, “On the complexity of finite sequences”, IEEE Trans., IT-22 (1976) pgs. 75–81.
A still further approach that may be applied to EEG signal analysis is fractal spectrum analysis based on chaos theory. In fractal spectrum analysis, the EEG signal is divided into a harmonic component and a fractal component. The harmonic component includes the simple frequencies whereas the fractal component contains the part which is invariant under scaling in time. It has been found that the fractal exponent Beta which corresponds to the frequency power law 1/fβ increases consistently in the course of deepening anesthesia.